Title

Author

Date of Award

8-2015

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Mechanical Engineering

Advisor

Summers, Joshua D.

Committee Member

Duarte, Rodrigo M.

Committee Member

Kurz, Mary E.

Abstract

In the Graph Complexity Connectivity Method (GCCM), twenty nine complexity metrics applied against engineering design graphs are used to create surrogate prediction models of engineering design representations (assembly models and function structures) for given product performance values (assembly time and market value). The performance of these prediction models has been previously assessed solely based on accuracy. In this thesis, the predictive precision of the surrogate models is evaluated in order to assess the GCCM's ability to generate consistent results under the same conditions. The Assembly Model - Assembly Time (AM-AT) prediction model performed the best in terms of both accuracy and precision. This demonstrates that when given assembly models, one can consistently predict accurate assembly times. Further, a sensitivity analysis is conducted to identify the significant complexity metrics in the estimation of the performance values, assembly time and market value. The results of the analysis suggest that for each prediction model, there exists at least one metric from each complexity class (size, interconnection, centrality, and decomposition) which is identified as a significant predictor. Two of the twenty nine complexity metrics are found to be significant for all four prediction models: number of elements and density of the in-core numbers. The significant complexity metrics were used to create simplified surrogate models to predict the product performance values. The test results indicate that the precision of the prediction models increases but the accuracy decreases when the unique significant metric sets are used. Finally, three experiments are conducted in order to investigate the effect of manipulation of the significant complexity metrics in predicting the performance values. The results suggest that the significant metric sets perform better in predicting the product performance values as compared to the manipulated metric sets of either union or intersection of metrics.